Modelli per task

Tutte le famiglie incluse nella libreria, generate dal registro dei modelli v1.7.0: 115 famiglie su 22 task.

Rilevamento

47 famiglie

Box attorno agli oggetti. Il task per cui è costruita la maggior parte della libreria.

⭐RF-DETR

n, s, m, l for detection and oriented boxes; n through xx for segmentation; x for pose

⭐YOLOv9

yolo9: t, s, m, c at 640 px; yolo9_p2: t, s at 640 px

D-FINE

n, s, m, l, x at 640 px

DEIM

deim: n, s, m, l, x at 640 px

EdgeCrafter

s, m, l, x at 640 px

RT-DETR

rtdetr: r18, r34, r50, r50m, r101, l, x at 640 px; rtdetrv2: r18, r34, r50, r50m, r101 at 640 px; rtdetrv4: s, m, l, x at 640 px

YOLO-NAS

s, m, l at 640 px

Dome-DETR

s, m, l at 800 px

PicoDet

s, m, l at 320 to 640 px

PP-YOLOE

s, m, l, x at 640 px

RTMDet

t, s, m, l, x at 640 px

YOLOv7

b at 640 px

YOLOX

n, t, s, m, l, x at 416 to 640 px

Florence-2

base, large at 768 px

Gemma 4

e2b, e4b at 896 px

Grounding DINO

t, b at 800 px

InternVL3

1b, 2b, 8b at 448 px

Kosmos-2

224 at 224 px

LFM2-VL

450m, 3b at 512 px

LocateAnything

3b at 2500 px

Moondream

2, 3 at 384 to 512 px

North Micro Vision
OMDet-Turbo

t at 640 px

OV-DEIM

s, m, l at 640 px

OWLv2

b16, l14 at 960 to 1008 px

Qwen3-VL

2b, 4b, 8b at 1024 px

SenseNova-Vision

7b at 1024 px

SmolVLM2

500m at 512 px

WeVisOne-VL

2b, 4b at 1440 px

CenterNet

resdcn18, dla34 at 512 px

Deformable DETR

r50ss, r50ssdc5, r50, r50refine, r50twostage at 800 px

DETR

r50, r50dc5, r101, r101dc5 at 800 px

DINO-DETR

r50, r50s5, swinl at 800 px

EfficientDet
Faster R-CNN

n, s, m, l at 320 to 800 px

FCOS

r50 at 800 px

LW-DETR

t, s, m, l, x at 640 px

Mask R-CNN

r50 at 800 px

RetinaNet

r50, r50v2 at 800 px

SSD

300 at 300 px

YOLOv1

t, b at 448 px

YOLOv2

t, b at 416 to 608 px

YOLOv3

t, b, spp at 416 to 608 px

YOLOv4

t, b at 416 to 608 px

Segmentazione

15 famiglie

Maschere per singola istanza invece dei box.

⭐RF-DETR

n, s, m, l for detection and oriented boxes; n through xx for segmentation; x for pose

D-FINE

n, s, m, l, x at 640 px

EdgeCrafter

s, m, l, x at 640 px

RTMDet

t, s, m, l, x at 640 px

MobileSAM

tiny at 1024 px

PicoSAM3

pico at 96 px

SAM

base, large, huge at 1024 px

SAM 3

large at 1008 px

SenseNova-Vision

7b at 1024 px

WeVisOne-VL

2b, 4b at 1440 px

EoMT

s, b, l at 512 px

Mask R-CNN

r50 at 800 px

Keypoint

7 famiglie

Scheletri e landmark su ogni istanza rilevata.

⭐RF-DETR

n, s, m, l for detection and oriented boxes; n through xx for segmentation; x for pose

EdgeCrafter

s, m, l, x at 640 px

YOLO-NAS

s, m, l at 640 px

SenseNova-Vision

7b at 1024 px

DEKR

w32 at 640 px

Classificazione

15 famiglie

Un'etichetta per l'intera immagine, e i backbone dietro a tutto il resto.

ConvNeXt

t, s, b at 224 px

DINOv2

n, s, m, l at 518 px

EfficientNetV2

b0, b1, b2, b3 at 224 to 300 px

MobileNetV4

s, m, l at 224 to 256 px

ResNet

18, 34, 50, 101 at 224 px

V-JEPA 2

l256, h256, g256, g384 at 256 to 384 px

Perception Encoder
AlexNet

b at 224 px

Swin Transformer

t, s, b, l at 224 px

VGG

16, 19, 16bn, 19bn at 224 px

ViT

ti, s, b, l at 224 px

DeiT

t, s, b at 224 px

Box orientati

4 famiglie

Box ruotati, per le immagini aeree e per tutto ciò che non è allineato agli assi.

⭐RF-DETR

n, s, m, l for detection and oriented boxes; n through xx for segmentation; x for pose

RT-DETR

rtdetr: r18, r34, r50, r50m, r101, l, x at 640 px; rtdetrv2: r18, r34, r50, r50m, r101 at 640 px; rtdetrv4: s, m, l, x at 640 px

YOLO-NAS

s, m, l at 640 px

Profondità

8 famiglie

Distanza per pixel a partire da una sola immagine.

SenseNova-Vision

7b at 1024 px

Depth Anything 3

l at 504 px

Depth Anything V2

s, b, l, g at 518 px

Marigold V2

b at 1024 px

MiDaS

s, l at 256 to 384 px

ZipDepth

b, bnpu at 384 px

Segmentazione semantica

10 famiglie

Una classe per ogni pixel, senza separare le istanze.

DINOv2

n, s, m, l at 518 px

LingBot-Vision
EoMT

s, b, l at 512 px

FCN

r50, r101 at 520 px

PIDNet

s, m, l at 1024 px

Segmentazione panottica

2 famiglie

Maschere semantiche e di istanza in un solo passaggio.

SenseNova-Vision

7b at 1024 px

EoMT

s, b, l at 512 px

Punti e conteggio

9 famiglie

Centroidi invece dei box, abbastanza leggeri da girare sui microcontrollori.

Florence-2 grounding

base, large at 768 px

LocateAnything

3b at 2500 px

Molmo2

4b, 8b at 378 px

Moondream

2, 3 at 384 to 512 px

Qwen3-VL grounding

2b, 4b, 8b at 1024 px

SenseNova-Vision

7b at 1024 px

ShowUI

2b at 1344 px

WeVisOne-VL

2b, 4b at 1440 px

Riconoscimento del testo

3 famiglie

Trovare e leggere il testo in un'immagine.

SenseNova-Vision

7b at 1024 px

WeVisOne-VL

2b, 4b at 1440 px

PP-OCRv5

t, l at 960 px

Embedding

7 famiglie

Vettori per ricerca, clustering e retrieval.

DINOv2

n, s, m, l at 518 px

V-JEPA 2

l256, h256, g256, g384 at 256 to 384 px

Perception Encoder
LeVJEPA

l at 224 px

LibreFaceRec

Sguardo

1 famiglie

Dove sta guardando una persona.

L2CS-Net

r18, r34, r50, r101, r152 at 448 px

Rilevamento dei bordi

3 famiglie

Contorni e confini.

DexiNed

b at 352 px

TEED

t at 352 px

Normali alle superfici

3 famiglie

In che direzione è orientata ogni superficie.

Marigold V2

b at 1024 px

MoGe-2

s, b, l at 518 px

Scontornamento

5 famiglie

Ritagli con canale alfa, capelli e bordi inclusi.

WeVisOne-VL

2b, 4b at 1440 px

BEN2

b at 1024 px

BiRefNet

t, l at 1024 px

FeyNobg

l at 1024 px

ViTMatte

s at 512 px

Restauro

7 famiglie

Riduzione del rumore, deblurring e upscaling.

NAFNet

s, l at 256 px

HVI-CIDNet

t at 256 px

QuickSRNet

m2 at 64 px

Real-ESRGAN

x4, x2, x4t at 64 px

SwinIR

s, m, l at 64 px

Mesh 3D

1 famiglie

Geometria 3D a partire da un'immagine.

SAM 3D Body

d3, h at 512 px

Robot policies

Robot policies

1 famiglie

Action chunks from camera observations and robot state.

LibreVLA

smolvla: base at 512 px; act_policy: base at 224 px; diffusion_policy: base at 224 px

Albedo

Albedo

1 famiglie

Intrinsic surface color in linear RGB.

Marigold V2

b at 1024 px

3D detection

3D detection

4 famiglie

Camera-frame cuboids from a single image.

DetAny3D

h at 896 px

FCOS3D

r101 at 1600 px

WildDet3D

l at 1008 px

Image generation

Image generation

2 famiglie

An image from a text prompt, with the seed that makes it again.

FLUX.2 klein

4b at 1024 px

SANA-Sprint
3D generation

3D generation

1 famiglie

The whole object as 3D Gaussian splats, from one picture of it.

Speridlabs Idol